Small- and Medium-sized Enterprises (SMEs) are a cornerstone of labour markets, accounting for over 99% of companies and 60% of business sector employment in OECD economies. Yet they are especially vulnerable to labour shortages and skill gaps, which dampen their growth prospects, their competitiveness and their resilience in the face of change. This study explores the potential for generative AI – tools that generate text, images, video or audio, such as ChatGPT, Copilot and Midjourney – to help SMEs address these challenges, by improving employee performance or by performing tasks that employees cannot do.
In late 2024, the OECD surveyed over 5 000 SMEs in Austria, Canada, Germany, Ireland, Japan, Korea and the United Kingdom, interviewing the individual within each SME with the best overview of the technologies used in the company (often the owner or manager). The survey delivers representative cross-country data on how SMEs use generative AI, how its use may be helping to address labour and skill needs, and how SMEs are preparing employees to use generative AI.
Generative AI has democratised the use of AI. While AI used to be the preserve of large firms, lower costs and data requirements mean that nearly a third of SMEs now use generative AI. It is used (by the respondent or a colleague) in 31% of SMEs, ranging from 24% in Japan to 39% in Germany. While SMEs in service sectors are the most likely to use generative AI, use cases are observed in every sector, underscoring the wide applicability of generative AI and its potential to impact workers across the entire economy.
Generative AI helps SMEs achieve more, mainly by improving employee performance. 65% of SMEs using generative AI report that it helped increase employee performance, more than say it enabled them to scale up (35%), to compete with larger companies (29%) or to increase revenue (26%).
Generative AI helps SMEs compensate for skill gaps and labour shortages. Among SMEs that use generative AI and have experienced a skill gap, 39% say that generative AI helped compensate for it. This figure is even higher (46%) where SMEs report that generative AI has improved employee performance, indicating that generative AI is particularly beneficial where a lack of skills is the main constraint for employee performance. SMEs not using generative AI are also optimistic that it could help them address skill gaps (42%) and worker shortages (24%), suggesting some untapped potential.
For a third of SMEs, using generative AI has reduced staff workload, or the entrepreneur’s own workload in the case of a one‑person business. In cases of overwork, technology that reduces workload by automating tasks and generating efficiencies may be very welcome. In addition, 14% of SMEs say generative AI has reduced their reliance on external contractors, possibly because it enables SMEs to perform tasks they previously would have outsourced.
Yet generative AI does not appear to lead SMEs to cut jobs. The vast majority of SMEs (83%) report that generative AI has had no effect on overall staff need. While AI might be expected to increase staff need by boosting productivity and consumer demand or decrease staff need by automating tasks, there are only modest signs of change, suggesting that SMEs prefer to wait before making internal staffing adjustments. 6% of SMEs report an increase in staff needs while 9% report a decrease.
Skills are essential for SMEs to use generative AI effectively. Although generative AI performs impressively on many cognitive tasks, it does not remove the need for skilled workers. Twice as many SMEs say that generative AI increases skills needs (20%) – i.e. increases the need for highly skilled workers relative to lower-skilled workers – as say that it decreases them (9%). For workers, data analysis and interpretation skills and creativity and innovation skills are perceived as the skills that have increased the most in importance due to generative AI.
Barriers to adopting generative AI include: unsuitability to the SME’s work (reported by 57% of non-adopters); concern about copyright, legal or regulatory issues (54%) and about what happens to the information fed into generative AI models (52%); and a lack of skills among employees (50%). Most SMEs (86%) hold neutral or positive attitudes toward generative AI, while only 2% prohibit its use. Attitudes towards generative AI do not therefore appear to be a major barrier.
Governments can help SMEs unlock the potential of generative AI, help close digital and skills gaps between SMEs and larger firms, and help ensure that any gains from generative AI are broadly shared across the economy and the workforce. Currently, a third or fewer of SMEs using generative AI are taking measures to train staff, set internal guidelines, or research copyright, legal, and regulatory issues. Government support can help these SMEs to harness the full potential of generative AI, while overcoming barriers to use for SMEs not currently using it. For instance, government intervention may be needed to promote AI literacy in the general population and to address the specific challenges that SMEs face in investing in training. In the survey, SMEs particularly welcome government support in the form of training, financial assistance, information campaigns and business mentoring.